{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-10-15T03:08:52.355700Z",
     "start_time": "2025-10-15T03:08:52.339107Z"
    }
   },
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-15T03:12:17.160036Z",
     "start_time": "2025-10-15T03:12:17.122404Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data = pd.read_csv('../../dataset/苹果品质数据集.csv', encoding='gbk')\n",
    "data"
   ],
   "id": "e5da2cb94f49b3f4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        序号      果实大小      果实重量      水果甜度     水果松脆度    水果多汁程度  Ripeness  \\\n",
       "0        0 -3.970049 -2.512336  5.346330 -1.012009  1.844900  0.329840   \n",
       "1        1 -1.195217 -2.839257  3.664059  1.588232  0.853286  0.867530   \n",
       "2        2 -0.292024 -1.351282 -1.738429 -0.342616  2.838636 -0.038033   \n",
       "3        3 -0.657196 -2.271627  1.324874 -0.097875  3.637970 -3.413761   \n",
       "4        4  1.364217 -1.296612 -0.384658 -0.553006  3.030874 -1.303849   \n",
       "...    ...       ...       ...       ...       ...       ...       ...   \n",
       "3995  3995  0.059386 -1.067408 -3.714549  0.473052  1.697986  2.244055   \n",
       "3996  3996 -0.293118  1.949253 -0.204020 -0.640196  0.024523 -1.087900   \n",
       "3997  3997 -2.634515 -2.138247 -2.440461  0.657223  2.199709  4.763859   \n",
       "3998  3998 -4.008004 -1.779337  2.366397 -0.200329  2.161435  0.214488   \n",
       "3999  3999  0.278540 -1.715505  0.121217 -1.154075  1.266677 -0.776571   \n",
       "\n",
       "          水果酸度 水果品质  \n",
       "0    -0.491590    好  \n",
       "1    -0.722809    好  \n",
       "2     2.621636    坏  \n",
       "3     0.790723    好  \n",
       "4     0.501984    好  \n",
       "...        ...  ...  \n",
       "3995  0.137784    坏  \n",
       "3996  1.854235    好  \n",
       "3997 -1.334611    坏  \n",
       "3998 -2.229720    好  \n",
       "3999  1.599796    好  \n",
       "\n",
       "[4000 rows x 9 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>果实大小</th>\n",
       "      <th>果实重量</th>\n",
       "      <th>水果甜度</th>\n",
       "      <th>水果松脆度</th>\n",
       "      <th>水果多汁程度</th>\n",
       "      <th>Ripeness</th>\n",
       "      <th>水果酸度</th>\n",
       "      <th>水果品质</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.970049</td>\n",
       "      <td>-2.512336</td>\n",
       "      <td>5.346330</td>\n",
       "      <td>-1.012009</td>\n",
       "      <td>1.844900</td>\n",
       "      <td>0.329840</td>\n",
       "      <td>-0.491590</td>\n",
       "      <td>好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.195217</td>\n",
       "      <td>-2.839257</td>\n",
       "      <td>3.664059</td>\n",
       "      <td>1.588232</td>\n",
       "      <td>0.853286</td>\n",
       "      <td>0.867530</td>\n",
       "      <td>-0.722809</td>\n",
       "      <td>好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.292024</td>\n",
       "      <td>-1.351282</td>\n",
       "      <td>-1.738429</td>\n",
       "      <td>-0.342616</td>\n",
       "      <td>2.838636</td>\n",
       "      <td>-0.038033</td>\n",
       "      <td>2.621636</td>\n",
       "      <td>坏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.657196</td>\n",
       "      <td>-2.271627</td>\n",
       "      <td>1.324874</td>\n",
       "      <td>-0.097875</td>\n",
       "      <td>3.637970</td>\n",
       "      <td>-3.413761</td>\n",
       "      <td>0.790723</td>\n",
       "      <td>好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.364217</td>\n",
       "      <td>-1.296612</td>\n",
       "      <td>-0.384658</td>\n",
       "      <td>-0.553006</td>\n",
       "      <td>3.030874</td>\n",
       "      <td>-1.303849</td>\n",
       "      <td>0.501984</td>\n",
       "      <td>好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>3995</td>\n",
       "      <td>0.059386</td>\n",
       "      <td>-1.067408</td>\n",
       "      <td>-3.714549</td>\n",
       "      <td>0.473052</td>\n",
       "      <td>1.697986</td>\n",
       "      <td>2.244055</td>\n",
       "      <td>0.137784</td>\n",
       "      <td>坏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>3996</td>\n",
       "      <td>-0.293118</td>\n",
       "      <td>1.949253</td>\n",
       "      <td>-0.204020</td>\n",
       "      <td>-0.640196</td>\n",
       "      <td>0.024523</td>\n",
       "      <td>-1.087900</td>\n",
       "      <td>1.854235</td>\n",
       "      <td>好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>3997</td>\n",
       "      <td>-2.634515</td>\n",
       "      <td>-2.138247</td>\n",
       "      <td>-2.440461</td>\n",
       "      <td>0.657223</td>\n",
       "      <td>2.199709</td>\n",
       "      <td>4.763859</td>\n",
       "      <td>-1.334611</td>\n",
       "      <td>坏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3998</td>\n",
       "      <td>-4.008004</td>\n",
       "      <td>-1.779337</td>\n",
       "      <td>2.366397</td>\n",
       "      <td>-0.200329</td>\n",
       "      <td>2.161435</td>\n",
       "      <td>0.214488</td>\n",
       "      <td>-2.229720</td>\n",
       "      <td>好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>3999</td>\n",
       "      <td>0.278540</td>\n",
       "      <td>-1.715505</td>\n",
       "      <td>0.121217</td>\n",
       "      <td>-1.154075</td>\n",
       "      <td>1.266677</td>\n",
       "      <td>-0.776571</td>\n",
       "      <td>1.599796</td>\n",
       "      <td>好</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4000 rows × 9 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-15T03:12:19.717990Z",
     "start_time": "2025-10-15T03:12:19.699240Z"
    }
   },
   "cell_type": "code",
   "source": "data.columns",
   "id": "a60279e47177a6dd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['序号', '果实大小', '果实重量', '水果甜度', '水果松脆度', '水果多汁程度', 'Ripeness', '水果酸度',\n",
       "       '水果品质'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-15T03:32:41.446503Z",
     "start_time": "2025-10-15T03:32:40.522131Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 定义特征和目标变量\n",
    "features = ['果实大小', '果实重量', '水果甜度', '水果松脆度', '水果多汁程度']\n",
    "X = data[features]  # 特征\n",
    "y = data['水果品质']  # 目标变量\n",
    "\n",
    "# 功能四：划分训练集（80%）和测试集（20%）\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42, stratify=y  # 分层抽样保持类别比例\n",
    ")\n",
    "\n",
    "# 定义三种核函数\n",
    "kernels = ['linear', 'poly', 'rbf']\n",
    "clf = None\n",
    "accuracies = {}\n",
    "for kernel in kernels:\n",
    "    # 创建SVM分类器\n",
    "    clf = SVC(kernel=kernel, random_state=42)\n",
    "    # 训练模型\n",
    "    clf.fit(X_train, y_train)\n",
    "    # 预测\n",
    "    y_pred = clf.predict(X_test)\n",
    "    # 计算准确率\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    accuracies[kernel] = acc\n",
    "\n",
    "# 输出最终结果\n",
    "for kernel, acc in accuracies.items():\n",
    "    print(f\"{kernel} 核函数准确率: {acc:.4f}\")"
   ],
   "id": "626ae3aee6c3d64e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear 核函数准确率: 0.7312\n",
      "poly 核函数准确率: 0.7338\n",
      "rbf 核函数准确率: 0.7925\n"
     ]
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-15T03:37:44.003741Z",
     "start_time": "2025-10-15T03:32:43.592627Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import tkinter as tk\n",
    "from tkinter import messagebox\n",
    "import threading\n",
    "import pyttsx3\n",
    "\n",
    "\n",
    "def predict_fruit_quality(size, weight, sweetness, crispness, juiciness, result_label):\n",
    "    print(f\"\\n📥 用户输入:\")\n",
    "    print(f\"  果实大小: {size}\")\n",
    "    print(f\"  果实重量: {weight}\")\n",
    "    print(f\"  水果甜度: {sweetness}\")\n",
    "    print(f\"  水果松脆度: {crispness}\")\n",
    "    print(f\"  水果多汁程度: {juiciness}\")\n",
    "\n",
    "    try:\n",
    "        # 构造输入特征向量（5个数值特征）\n",
    "        input_X = [[size, weight, sweetness, crispness, juiciness]]  # shape: (1, 5)\n",
    "\n",
    "        # 模型预测（输出 OneHot 编码结果）\n",
    "        y_pred_onehot = clf.predict(input_X)  # shape: (1, num_classes)\n",
    "\n",
    "        # 解码 OneHot 编码结果（获取预测类别）\n",
    "        y_pred_label = clf.classes_[y_pred_onehot.argmax()]\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 预测错误: {e}\")\n",
    "        result_label.config(text=\"❌ 预测失败，请检查模型或输入\", fg=\"red\")\n",
    "        return\n",
    "\n",
    "    print(f\"🎯 预测结果: {y_pred_label}\")\n",
    "\n",
    "    # 设置颜色\n",
    "    color = \"green\" if \"好\" in str(y_pred_label) else \"red\"\n",
    "    result_text = f\"🍎 水果品质预测: {y_pred_label}\"\n",
    "    result_label.config(text=result_text, fg=color)\n",
    "\n",
    "    # 语音播报\n",
    "    def speak():\n",
    "        engine = pyttsx3.init()\n",
    "        engine.say(f\"水果品质是 {y_pred_label}\")\n",
    "        engine.runAndWait()\n",
    "\n",
    "    threading.Thread(target=speak, daemon=True).start()\n",
    "\n",
    "\n",
    "# GUI 界面（Tkinter）\n",
    "def create_gui():\n",
    "    root = tk.Tk()\n",
    "    root.title(\"🍎 水果品质预测系统\")\n",
    "    root.geometry(\"600x450\")\n",
    "    root.resizable(False, False)\n",
    "\n",
    "    tk.Label(root, text=\"水果品质预测系统\", font=(\"Arial\", 16, \"bold\")).pack(pady=10)\n",
    "\n",
    "    frame = tk.Frame(root)\n",
    "    frame.pack(pady=10)\n",
    "\n",
    "    labels = [\"果实大小\", \"果实重量\", \"水果甜度\", \"水果松脆度\", \"水果多汁程度\"]\n",
    "    entries = {}\n",
    "    for i, label in enumerate(labels):\n",
    "        tk.Label(frame, text=f\"{label}:\", anchor=\"w\", width=15).grid(row=i, column=0, sticky=\"w\", pady=6)\n",
    "        entry = tk.Entry(frame, width=35)\n",
    "        entry.grid(row=i, column=1, padx=10)\n",
    "        entries[label] = entry\n",
    "\n",
    "    result_label = tk.Label(root, text=\"\", font=(\"Arial\", 12), wraplength=500)\n",
    "    result_label.pack(pady=10)\n",
    "\n",
    "    def on_predict():\n",
    "        vals = {k: v.get().strip() for k, v in entries.items()}\n",
    "        if not all(vals.values()):\n",
    "            messagebox.showerror(\"输入错误\", \"请填写所有字段！\")\n",
    "            return\n",
    "\n",
    "        try:\n",
    "            size = float(vals[\"果实大小\"])\n",
    "            weight = float(vals[\"果实重量\"])\n",
    "            sweetness = float(vals[\"水果甜度\"])\n",
    "            crispness = float(vals[\"水果松脆度\"])\n",
    "            juiciness = float(vals[\"水果多汁程度\"])\n",
    "        except ValueError:\n",
    "            messagebox.showerror(\"格式错误\", \"请输入有效的数字！\")\n",
    "            return\n",
    "\n",
    "        predict_fruit_quality(size, weight, sweetness, crispness, juiciness, result_label)\n",
    "\n",
    "    # 预测按钮\n",
    "    tk.Button(root, text=\"🎯 预测品质\", command=on_predict,\n",
    "              bg=\"#4CAF50\", fg=\"white\", font=(\"Arial\", 12), width=15).pack(pady=10)\n",
    "\n",
    "    # 退出按钮\n",
    "    tk.Button(root, text=\"退出\", command=root.quit, font=(\"Arial\", 10)).pack(pady=5)\n",
    "\n",
    "    root.mainloop()\n",
    "\n",
    "\n",
    "# 启动应用\n",
    "if __name__ == \"__main__\":\n",
    "    create_gui()"
   ],
   "id": "33868e064769310f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📥 用户输入:\n",
      "  果实大小: -3.970048523\n",
      "  果实重量: -2.512336381\n",
      "  水果甜度: 5.346329613\n",
      "  水果松脆度: -1.012008712\n",
      "  水果多汁程度: 1.844900361\n",
      "🎯 预测结果: 坏\n"
     ]
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-15T03:32:04.937897Z",
     "start_time": "2025-10-15T03:32:03.597879Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 选择特征\n",
    "features = ['果实大小', '果实重量', '水果甜度', '水果松脆度', '水果多汁程度']\n",
    "target = '水果品质'\n",
    "\n",
    "# 确保目标变量是字符串\n",
    "data[target] = data[target].astype(str)\n",
    "\n",
    "# 创建图形\n",
    "fig, axes = plt.subplots(2, 3, figsize=(16, 10))\n",
    "axes = axes.ravel()\n",
    "\n",
    "# 特征英文映射（用于标签）\n",
    "feature_names_en = {\n",
    "    '果实大小': 'Fruit Size',\n",
    "    '果实重量': 'Fruit Weight',\n",
    "    '水果甜度': 'Sweetness',\n",
    "    '水果松脆度': 'Crispness',\n",
    "    '水果多汁程度': 'Juiciness'\n",
    "}\n",
    "\n",
    "for i, col in enumerate(features):\n",
    "    if i >= 5:\n",
    "        axes[i].axis('off')\n",
    "        continue\n",
    "    sns.boxplot(data=data, x=target, y=col, ax=axes[i], palette=\"Set3\")\n",
    "    axes[i].set_title(f'Distribution of {feature_names_en[col]} by Quality', fontsize=12)\n",
    "    axes[i].set_xlabel('Quality')\n",
    "    axes[i].set_ylabel(feature_names_en[col])\n",
    "\n",
    "# 隐藏第六个子图\n",
    "axes[5].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle(\"Figure 1: Feature Distributions by Fruit Quality\", fontsize=16, y=0.98)\n",
    "plt.subplots_adjust(top=0.9)\n",
    "plt.show()"
   ],
   "id": "c9854aa4ed53db22",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lzj36\\AppData\\Local\\Temp\\ipykernel_24792\\3330090943.py:28: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=data, x=target, y=col, ax=axes[i], palette=\"Set3\")\n",
      "C:\\Users\\lzj36\\AppData\\Local\\Temp\\ipykernel_24792\\3330090943.py:28: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=data, x=target, y=col, ax=axes[i], palette=\"Set3\")\n",
      "C:\\Users\\lzj36\\AppData\\Local\\Temp\\ipykernel_24792\\3330090943.py:28: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=data, x=target, y=col, ax=axes[i], palette=\"Set3\")\n",
      "C:\\Users\\lzj36\\AppData\\Local\\Temp\\ipykernel_24792\\3330090943.py:28: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=data, x=target, y=col, ax=axes[i], palette=\"Set3\")\n",
      "C:\\Users\\lzj36\\AppData\\Local\\Temp\\ipykernel_24792\\3330090943.py:28: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=data, x=target, y=col, ax=axes[i], palette=\"Set3\")\n",
      "C:\\Users\\lzj36\\AppData\\Local\\Temp\\ipykernel_24792\\3330090943.py:36: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\lzj36\\AppData\\Local\\Temp\\ipykernel_24792\\3330090943.py:36: UserWarning: Glyph 22351 (\\N{CJK UNIFIED IDEOGRAPH-574F}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "E:\\python_environments\\ai\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\python_environments\\ai\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 22351 (\\N{CJK UNIFIED IDEOGRAPH-574F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1000 with 6 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-15T03:32:16.718639Z",
     "start_time": "2025-10-15T03:32:16.344828Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# 构造特征子集\n",
    "X = data[features]\n",
    "\n",
    "# 计算相关系数矩阵\n",
    "corr_matrix = X.corr()\n",
    "\n",
    "# 绘图\n",
    "plt.figure(figsize=(8, 6))\n",
    "mask = np.triu(np.ones_like(corr_matrix, dtype=bool))  # 上三角遮罩\n",
    "sns.heatmap(corr_matrix,\n",
    "            annot=True,\n",
    "            cmap='coolwarm',\n",
    "            center=0,\n",
    "            fmt='.2f',\n",
    "            square=True,\n",
    "            mask=mask,\n",
    "            cbar_kws={\"shrink\": .8},\n",
    "            xticklabels=[feature_names_en[col] for col in features],\n",
    "            yticklabels=[feature_names_en[col] for col in features])\n",
    "\n",
    "plt.title(\"Figure 2: Correlation Heatmap of Features\", fontsize=14, pad=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=0)\n",
    "plt.show()"
   ],
   "id": "98480f2e4e031270",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-15T03:32:20.641885Z",
     "start_time": "2025-10-15T03:32:20.634868Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "fca06b7f92d9e7bf",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
